{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8fd731dc-8374-476e-8619-246bc4c3b003",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w= [1702.22424242] b= [-922.33333333]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "x = np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]])\n",
    "y = np.array([[2000],[2200],[4900],[3221],[6834],[10567],[9566],[15678],[13644],[15789]])\n",
    "model = LinearRegression()\n",
    "model.fit(x,y)\n",
    "print(\"w=\",model.coef_[0],\"b=\",model.intercept_)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52bfe226-962b-4d17-a500-5f20e27d2c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "y2 = model.predict(x)\n",
    "plt.xlabel(\"工龄/年\")\n",
    "plt.ylabel(\"平均工资/元\")\n",
    "plt.rcParams[\"font.sans-serif\"] = \"Simhei\"\n",
    "plt.axis([1,10,1000,16000])\n",
    "\n",
    "plt.scatter(x,y,s=60,c=\"k\",marker=\"o\")\n",
    "plt.plot(x,y2,\"r-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db3ee4f1-d8af-4db2-b0b9-f7afde630e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "a= model.predict([[100]])\n",
    "print(a[0][0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
